Xcyt: a system for Remote Cytological Diagnosis and Prognosis of Breast Cancer W. N. Street
Representation of Nuclear Boundaries
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- 2.4Algorithmic Improvements
- 2.5Nuclear Morphometric Features
- 3Diagnosis
2.3Representation of Nuclear BoundariesThe desired quantification of nuclear shape requires a very precise representation of boundaries. These are generated with the aid of a deformable spline technique known as a snake [21]. The snake seeks to minimize an energy function defined over the arclength of the curve. The energy function is defined in such a way that the minimum value should occur when the curve accurately corresponds to the boundary of a nucleus. This energy function is defined as follows:  (1)
Here E represents the total energy integrated along the arclength s of the spline. The energy is a weighted sum of three components Econt, Ecurv and Eimage with respective weights , and . The continuity energy Econt penalizes discontinuities in the curve. The curvature energy Ecurv penalizes areas of the curve with abnormally high or low curvature, so that the curve tends to form a circle in the absence of other information. The spline is tied to the underlying image using the image energy term Eimage. Here we again use a Sobel edge detector to measure the edge magnitude and direction at each point along the curve. Points with a strong gray-scale discontinuity in the appropriate direction are given low energy; others are given a high energy. The constants are empirically set so that this term dominates. Hence, the snake will settle along a boundary when edge information is available. The weight is set high enough that, in areas of occlusion or poor focus, the snake forms an arc, in a manner similar to how a person might outline the same object. This results in a small degree of “rounding” of the resulting contour. Our experiments indicate that this reduces operator dependence and makes only a small change in the value of the computed features. The snakes are initialized using the elliptic approximations found by the Hough transform described in the previous section. They may also be initialized manually by the operator using the mouse pointer. To simplify the necessary processing, the energy function is computed at a number of discrete points along the curve. A greedy optimization method [40] is used to move the snake points to a local minimum of the energy space. 2.4Algorithmic ImprovementsThe two-stage approach of using the Hough transform for object detection and the snakes for boundary definition results in precise outlines of the well-defined nuclei in the cytological images. However, the Hough transform is very computationally expensive, requiring several minutes to search for nuclei in the observed size range. We have recently designed two heuristic approaches to reducing this computational load [23]. First, the user is given the option of performing the GHT on a scaled version of the image. This results in a rather imprecise location of the nuclei but runs about an order of magnitude faster. The GHT can then be performed on a small region of the full-sized image to precisely locate the suspected nucleus and determine the correct matching template. Our experiments indicate that this results in an acceptably small degradation of accuracy. 
Figure 5. Results of the nuclear location algorithm on two sample images. Second, we allow the GHT to be “seeded” with an initial boundary initialized by the user. The GHT then searches only for nuclei of about the same size as that drawn by the user. This results in a reduced search space and, again, a significant speed-up with minimal accuracy reduction. Results on two dissimilar images are shown in Figure 5. Snakes that fail to successfully conform to a nuclear boundary can be manually deleted by the user and initialized using the mouse pointer. The use of these semi-automatic object recognition techniques minimizes the dependence on a careful operator, resulting in more reliable and repeatable results. 2.5Nuclear Morphometric FeaturesThe following nuclear features are computed for each identified nucleus [38].
The system computes the mean value, extreme or largest value, and standard error of each of these ten features, resulting in a total of 30 predictive features for each sample. These features are used as the input in the predictive methods described in the next section. 3DiagnosisWe frame the diagnosis problem as that of determining whether a previously detected breast lump is benign or malignant. There are three popular methods for diagnosing breast cancer: mammography, FNA with visual interpretation, and surgical biopsy. The reported sensitivity (i.e., the ability to correctly diagnose cancer when the disease is present) of mammography varies from 68% to 79% [14], of FNA with visual interpretation from 65% to 98% [15], and of surgical biopsy close to 100%. Therefore, mammography lacks sensitivity, FNA sensitivity varies widely, and surgical biopsy, although accurate, is invasive, time consuming, and costly. The goal of the diagnostic aspect of our research is to develop a relatively objective system that diagnoses FNAs with an accuracy that approaches the best achieved visually. Download 101.24 Kb. Share with your friends:
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